MultiQC Report

A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

This report has been generated by the nf-core/scrnaseq analysis pipeline. For information about how to interpret these results, please see the documentation.

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Report generated on 2025-01-21, 17:56 CET based on data in: /nfs/scratch/nf-core_work/nfs.data.COST_IBD.VASA-Seq.scrnaseq.run/23/a4bafb71d4ef631e58dcb845187bee

General Statistics

Showing ⁰/₁₃₀ rows and ⁶/₁₂ columns.

Sample Name	Total reads	Aligned	Aligned	Uniq aligned	Uniq aligned	Multimapped	Dups	GC	Avg len	Median len	Failed	Seqs
24L007779_S1_L001	9.5M	3.8M	40.3%	3.0M	31.9%	0.8M
24L007779_S1_L001_1							2.7%	45.0%	151bp	151bp	27%	9.5M
24L007779_S1_L001_2							30.9%	49.0%	151bp	151bp	27%	9.5M
24L007779_S1_L002	9.7M	3.9M	40.3%	3.1M	31.9%	0.8M
24L007779_S1_L002_1							2.9%	44.0%	151bp	151bp	18%	9.7M
24L007779_S1_L002_2							30.2%	49.0%	151bp	151bp	27%	9.7M
24L007780_S2_L001	8.7M	4.0M	46.3%	3.3M	37.9%	0.7M
24L007780_S2_L001_1							3.8%	39.0%	151bp	151bp	27%	8.7M
24L007780_S2_L001_2							30.9%	45.0%	151bp	151bp	27%	8.7M
24L007780_S2_L002	8.9M	4.1M	46.1%	3.4M	37.8%	0.7M
24L007780_S2_L002_1							4.0%	39.0%	150bp	150bp	18%	8.9M
24L007780_S2_L002_2							30.4%	45.0%	151bp	151bp	27%	8.9M
24L007781_S3_L001	8.6M	3.8M	44.4%	3.1M	36.5%	0.7M
24L007781_S3_L001_1							3.6%	42.0%	151bp	151bp	27%	8.6M
24L007781_S3_L001_2							26.7%	47.0%	151bp	151bp	27%	8.6M
24L007781_S3_L002	8.7M	3.9M	44.3%	3.2M	36.4%	0.7M
24L007781_S3_L002_1							3.6%	42.0%	151bp	151bp	18%	8.7M
24L007781_S3_L002_2							26.1%	47.0%	151bp	151bp	27%	8.7M
24L007782_S4_L001	11.4M	5.3M	46.7%	4.4M	38.8%	0.9M
24L007782_S4_L001_1							3.4%	41.0%	151bp	151bp	27%	11.4M
24L007782_S4_L001_2							36.7%	47.0%	151bp	151bp	36%	11.4M
24L007782_S4_L002	11.6M	5.4M	46.7%	4.5M	38.8%	0.9M
24L007782_S4_L002_1							3.4%	41.0%	151bp	151bp	18%	11.6M
24L007782_S4_L002_2							35.9%	47.0%	151bp	151bp	36%	11.6M
24L007783_S5_L001	6.4M	2.8M	43.3%	2.3M	35.6%	0.5M
24L007783_S5_L001_1							3.7%	39.0%	151bp	151bp	27%	6.4M
24L007783_S5_L001_2							32.8%	45.0%	151bp	151bp	27%	6.4M
24L007783_S5_L002	6.5M	2.8M	43.2%	2.3M	35.6%	0.5M
24L007783_S5_L002_1							3.7%	39.0%	151bp	151bp	18%	6.5M
24L007783_S5_L002_2							32.0%	45.0%	151bp	151bp	27%	6.5M
24L007784_S6_L001	7.2M	5.1M	71.2%	4.6M	63.8%	0.5M
24L007784_S6_L001_1							3.7%	35.0%	151bp	151bp	18%	7.2M
24L007784_S6_L001_2							29.7%	45.0%	151bp	151bp	18%	7.2M
24L007784_S6_L002	7.3M	5.2M	71.2%	4.7M	63.8%	0.5M
24L007784_S6_L002_1							3.9%	35.0%	151bp	151bp	9%	7.3M
24L007784_S6_L002_2							29.1%	45.0%	151bp	151bp	18%	7.3M
2CLCmesc_0-0-3_S15	22.6M	8.8M	39.1%	7.7M	34.1%	1.1M
2CLCmesc_0-0-3_S15_1							8.4%	42.0%	100bp	100bp	36%	22.6M
2CLCmesc_0-0-3_S15_2							32.6%	54.0%	201bp	201bp	36%	22.6M
2CLCmesc_0-0-4_S16	17.2M	8.6M	49.8%	7.4M	43.1%	1.2M
2CLCmesc_0-0-4_S16_1							2.8%	41.0%	100bp	100bp	18%	17.2M
2CLCmesc_0-0-4_S16_2							34.7%	53.0%	201bp	201bp	36%	17.2M
2CLCmesc_003_RR_S3	11.2M	7.2M	64.4%	6.4M	57.1%	0.8M
2CLCmesc_003_RR_S3_1							3.1%	43.0%	100bp	100bp	18%	11.2M
2CLCmesc_003_RR_S3_2							70.7%	52.0%	201bp	201bp	45%	11.2M
2CLCmesc_004_RR_S4	11.9M	8.7M	73.0%	7.6M	64.1%	1.1M
2CLCmesc_004_RR_S4_1							1.8%	43.0%	100bp	100bp	18%	11.9M
2CLCmesc_004_RR_S4_2							72.4%	52.0%	201bp	201bp	45%	11.9M
2CLCmesc_011_RR_S18	12.6M	9.0M	71.2%	7.8M	61.7%	1.2M
2CLCmesc_011_RR_S18_1							1.9%	41.0%	100bp	101bp	18%	12.6M
2CLCmesc_011_RR_S18_2							66.4%	49.0%	201bp	201bp	45%	12.6M
2CLCmesc_016_RR_S17	9.9M	6.0M	60.5%	5.2M	52.1%	0.8M
2CLCmesc_016_RR_S17_1							2.6%	44.0%	101bp	101bp	36%	9.9M
2CLCmesc_016_RR_S17_2							60.1%	53.0%	201bp	201bp	45%	9.9M
2CLCmesc_1_RR_S9	3.5M	2.1M	61.0%	1.8M	52.9%	0.3M
2CLCmesc_1_RR_S9_1							2.1%	43.0%	100bp	101bp	18%	3.5M
2CLCmesc_1_RR_S9_2							65.5%	53.0%	201bp	201bp	45%	3.5M
2CLCmesc_1_S1	13.9M	2.9M	20.7%	2.5M	18.3%	0.3M
2CLCmesc_1_S1_1							3.8%	42.0%	100bp	100bp	36%	13.9M
2CLCmesc_1_S1_2							29.2%	58.0%	201bp	201bp	36%	13.9M
2CLCmesc_2_RR_S10	8.3M	4.8M	57.2%	4.1M	49.0%	0.7M
2CLCmesc_2_RR_S10_1							2.6%	43.0%	100bp	101bp	18%	8.3M
2CLCmesc_2_RR_S10_2							67.6%	53.0%	201bp	201bp	45%	8.3M
2CLCmesc_2_S2	11.3M	1.8M	16.1%	1.5M	13.6%	0.3M
2CLCmesc_2_S2_1							3.4%	43.0%	100bp	100bp	36%	11.3M
2CLCmesc_2_S2_2							26.4%	59.0%	201bp	201bp	36%	11.3M
2CLCmesc_3_RR_S11	13.2M	9.1M	68.6%	7.9M	59.5%	1.2M
2CLCmesc_3_RR_S11_1							3.5%	42.0%	100bp	100bp	18%	13.2M
2CLCmesc_3_RR_S11_2							53.7%	52.0%	201bp	201bp	36%	13.2M
2CLCmesc_3_S5	20.6M	12.5M	60.9%	10.9M	53.3%	1.6M
2CLCmesc_3_S5_1							4.5%	38.0%	100bp	100bp	27%	20.6M
2CLCmesc_3_S5_2							27.4%	48.0%	201bp	201bp	36%	20.6M
2CLCmesc_4_RR_S12	12.0M	5.6M	46.5%	4.8M	40.1%	0.8M
2CLCmesc_4_RR_S12_1							8.1%	42.0%	100bp	100bp	27%	12.0M
2CLCmesc_4_RR_S12_2							35.3%	56.0%	201bp	201bp	45%	12.0M
2CLCmesc_4_S6	15.9M	9.4M	59.1%	8.1M	51.0%	1.3M
2CLCmesc_4_S6_1							9.5%	36.0%	100bp	100bp	27%	15.9M
2CLCmesc_4_S6_2							34.2%	46.0%	201bp	201bp	45%	15.9M
2CLCmesc_5_RR_S13	6.7M	4.9M	72.6%	4.2M	62.7%	0.7M
2CLCmesc_5_RR_S13_1							4.1%	38.0%	100bp	100bp	18%	6.7M
2CLCmesc_5_RR_S13_2							43.1%	48.0%	201bp	201bp	36%	6.7M
2CLCmesc_5_S7	16.9M	12.9M	76.4%	11.2M	66.2%	1.7M
2CLCmesc_5_S7_1							5.2%	34.0%	100bp	100bp	18%	16.9M
2CLCmesc_5_S7_2							39.4%	43.0%	201bp	201bp	36%	16.9M
2CLCmesc_6_RR_S14	5.4M	1.9M	34.9%	1.5M	28.5%	0.3M
2CLCmesc_6_RR_S14_1							15.2%	37.0%	100bp	100bp	45%	5.4M
2CLCmesc_6_RR_S14_2							36.0%	49.0%	201bp	201bp	45%	5.4M
2CLCmesc_6_S8	9.3M	3.8M	40.8%	3.2M	34.1%	0.6M
2CLCmesc_6_S8_1							16.2%	35.0%	100bp	100bp	45%	9.3M
2CLCmesc_6_S8_2							33.7%	48.0%	201bp	201bp	45%	9.3M
combined_003	33.8M	16.0M	47.5%	14.1M	41.7%	1.9M
combined_003_1							8.4%	42.0%	100bp	100bp	36%	22.6M
combined_003_2							32.6%	54.0%	201bp	201bp	36%	22.6M
combined_003_3							3.1%	43.0%	100bp	100bp	18%	11.2M
combined_003_4							70.7%	52.0%	201bp	201bp	45%	11.2M
combined_004	29.0M	17.2M	59.3%	15.0M	51.7%	2.2M
combined_004_1							2.8%	41.0%	100bp	100bp	18%	17.2M
combined_004_2							34.7%	53.0%	201bp	201bp	36%	17.2M
combined_004_3							1.8%	43.0%	100bp	100bp	18%	11.9M
combined_004_4							72.4%	52.0%	201bp	201bp	45%	11.9M
combined_1	17.3M	5.0M	28.7%	4.4M	25.2%	0.6M
combined_1_1							3.8%	42.0%	100bp	100bp	36%	13.9M
combined_1_2							29.2%	58.0%	201bp	201bp	36%	13.9M
combined_1_3							2.1%	43.0%	100bp	101bp	18%	3.5M
combined_1_4							65.5%	53.0%	201bp	201bp	45%	3.5M
combined_2	19.6M	6.6M	33.6%	5.6M	28.6%	1.0M
combined_2_1							3.4%	43.0%	100bp	100bp	36%	11.3M
combined_2_2							26.4%	59.0%	201bp	201bp	36%	11.3M
combined_2_3							2.6%	43.0%	100bp	101bp	18%	8.3M
combined_2_4							67.6%	53.0%	201bp	201bp	45%	8.3M
combined_3	33.8M	21.6M	63.9%	18.8M	55.7%	2.8M
combined_3_1							4.5%	38.0%	100bp	100bp	27%	20.6M
combined_3_2							27.4%	48.0%	201bp	201bp	36%	20.6M
combined_3_3							3.5%	42.0%	100bp	100bp	18%	13.2M
combined_3_4							53.7%	52.0%	201bp	201bp	36%	13.2M
combined_4	27.9M	15.0M	53.7%	12.9M	46.3%	2.1M
combined_4_1							9.5%	36.0%	100bp	100bp	27%	15.9M
combined_4_2							34.2%	46.0%	201bp	201bp	45%	15.9M
combined_4_3							8.1%	42.0%	100bp	100bp	27%	12.0M
combined_4_4							35.3%	56.0%	201bp	201bp	45%	12.0M
combined_5	23.6M	17.8M	75.3%	15.4M	65.2%	2.4M
combined_5_1							5.2%	34.0%	100bp	100bp	18%	16.9M
combined_5_2							39.4%	43.0%	201bp	201bp	36%	16.9M
combined_5_3							4.1%	38.0%	100bp	100bp	18%	6.7M
combined_5_4							43.1%	48.0%	201bp	201bp	36%	6.7M
combined_6	14.7M	5.7M	38.6%	4.7M	32.1%	1.0M
combined_6_1							16.2%	35.0%	100bp	100bp	45%	9.3M
combined_6_2							33.7%	48.0%	201bp	201bp	45%	9.3M
combined_6_3							15.2%	37.0%	100bp	100bp	45%	5.4M
combined_6_4							36.0%	49.0%	201bp	201bp	45%	5.4M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: general_stats_table_table

Sort	Group	Column	Description	ID	Scale
\|\|	STAR	Total reads	Number of input reads	`star-total_reads`	read_count
\|\|	STAR	Aligned	Mapped reads	`star-mapped`	read_count
\|\|	STAR	Aligned	% Mapped reads	`star-mapped_percent`
\|\|	STAR	Uniq aligned	Uniquely mapped reads	`star-uniquely_mapped`	read_count
\|\|	STAR	Uniq aligned	% Uniquely mapped reads	`star-uniquely_mapped_percent`
\|\|	STAR	Multimapped	Multiple mapped reads	`star-multimapped`	read_count
\|\|	FastQC	Dups	% duplicate reads	`fastqc-percent_duplicates`
\|\|	FastQC	GC	Average % GC content	`fastqc-percent_gc`
\|\|	FastQC	Avg len	Average read length	`fastqc-avg_sequence_length`
\|\|	FastQC	Median len	Median read length	`fastqc-median_sequence_length`
\|\|	FastQC	Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`fastqc-percent_fails`
\|\|	FastQC	Seqs	Total sequences (millions)	`fastqc-total_sequences`

STAR

Universal RNA-seq aligner.URL: https://github.com/alexdobin/STARDOI: 10.1093/bioinformatics/bts635

Summary Statistics

Summary statistics from the STAR alignment

Showing ⁰/₃₈ rows and ¹⁰/₁₉ columns.

Sample Name	Total reads	Aligned	Aligned	Uniq aligned	Uniq aligned	Multimapped	Avg. read len	Avg. mapped len	Splices	Annotated splices	GT/AG splices	GC/AG splices	AT/AC splices	Non-canonical splices	Mismatch rate	Del rate	Del len	Ins rate	Ins len
24L007779_S1_L001	9.5M	3.8M	40.3%	3.0M	31.9%	0.8M	150.0bp	141.2bp	0.8M	0.7M	0.7M	0.0M	0.0M	0.0M	0.8%	0.1%	4.1bp	0.0%	1.6bp
24L007779_S1_L002	9.7M	3.9M	40.3%	3.1M	31.9%	0.8M	150.0bp	141.2bp	0.8M	0.7M	0.7M	0.0M	0.0M	0.0M	0.9%	0.1%	4.0bp	0.0%	1.6bp
24L007780_S2_L001	8.7M	4.0M	46.3%	3.3M	37.9%	0.7M	150.0bp	141.4bp	0.9M	0.9M	0.9M	0.0M	0.0M	0.0M	0.8%	0.1%	3.6bp	0.0%	1.4bp
24L007780_S2_L002	8.9M	4.1M	46.1%	3.4M	37.8%	0.7M	150.0bp	141.5bp	0.9M	0.9M	0.9M	0.0M	0.0M	0.0M	0.9%	0.1%	3.6bp	0.0%	1.4bp
24L007781_S3_L001	8.6M	3.8M	44.4%	3.1M	36.5%	0.7M	150.0bp	142.1bp	0.9M	0.8M	0.8M	0.0M	0.0M	0.0M	0.8%	0.1%	3.7bp	0.0%	1.4bp
24L007781_S3_L002	8.7M	3.9M	44.3%	3.2M	36.4%	0.7M	150.0bp	142.1bp	0.9M	0.8M	0.8M	0.0M	0.0M	0.0M	0.9%	0.1%	3.7bp	0.0%	1.4bp
24L007782_S4_L001	11.4M	5.3M	46.7%	4.4M	38.8%	0.9M	150.0bp	140.2bp	0.9M	0.8M	0.8M	0.0M	0.0M	0.0M	0.9%	0.1%	3.3bp	0.0%	1.5bp
24L007782_S4_L002	11.6M	5.4M	46.7%	4.5M	38.8%	0.9M	150.0bp	140.2bp	0.9M	0.8M	0.9M	0.0M	0.0M	0.0M	0.9%	0.1%	3.3bp	0.0%	1.5bp
24L007783_S5_L001	6.4M	2.8M	43.3%	2.3M	35.6%	0.5M	150.0bp	140.4bp	0.5M	0.4M	0.4M	0.0M	0.0M	0.0M	0.9%	0.1%	3.2bp	0.0%	1.4bp
24L007783_S5_L002	6.5M	2.8M	43.2%	2.3M	35.6%	0.5M	150.0bp	140.4bp	0.5M	0.4M	0.4M	0.0M	0.0M	0.0M	1.0%	0.1%	3.2bp	0.0%	1.4bp
24L007784_S6_L001	7.2M	5.1M	71.2%	4.6M	63.8%	0.5M	150.0bp	143.5bp	1.0M	1.0M	1.0M	0.0M	0.0M	0.0M	0.8%	0.1%	3.5bp	0.1%	1.5bp
24L007784_S6_L002	7.3M	5.2M	71.2%	4.7M	63.8%	0.5M	150.0bp	143.5bp	1.1M	1.0M	1.0M	0.0M	0.0M	0.0M	0.9%	0.1%	3.4bp	0.1%	1.5bp
2CLCmesc_0-0-3_S15	22.6M	8.8M	39.1%	7.7M	34.1%	1.1M	200.0bp	183.3bp	2.5M	2.4M	2.4M	0.0M	0.0M	0.1M	0.6%	0.1%	3.3bp	0.0%	1.5bp
2CLCmesc_0-0-4_S16	17.2M	8.6M	49.8%	7.4M	43.1%	1.2M	200.0bp	185.4bp	2.5M	2.4M	2.4M	0.0M	0.0M	0.1M	0.6%	0.1%	3.4bp	0.1%	1.8bp
2CLCmesc_003_RR_S3	11.2M	7.2M	64.4%	6.4M	57.1%	0.8M	200.0bp	183.7bp	0.5M	0.4M	0.4M	0.0M	0.0M	0.1M	0.8%	0.1%	3.5bp	0.1%	2.2bp
2CLCmesc_004_RR_S4	11.9M	8.7M	73.0%	7.6M	64.1%	1.1M	200.0bp	186.2bp	0.6M	0.5M	0.5M	0.0M	0.0M	0.1M	0.8%	0.1%	3.3bp	0.1%	2.2bp
2CLCmesc_011_RR_S18	12.6M	9.0M	71.2%	7.8M	61.7%	1.2M	200.0bp	183.0bp	0.6M	0.5M	0.5M	0.0M	0.0M	0.1M	0.8%	0.1%	3.4bp	0.1%	2.1bp
2CLCmesc_016_RR_S17	9.9M	6.0M	60.5%	5.2M	52.1%	0.8M	200.0bp	183.5bp	0.3M	0.3M	0.3M	0.0M	0.0M	0.0M	0.8%	0.1%	3.2bp	0.1%	2.2bp
2CLCmesc_1_RR_S9	3.5M	2.1M	61.0%	1.8M	52.9%	0.3M	200.0bp	182.3bp	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	1.3%	0.1%	5.0bp	0.4%	3.3bp
2CLCmesc_1_S1	13.9M	2.9M	20.7%	2.5M	18.3%	0.3M	200.0bp	183.3bp	0.8M	0.7M	0.8M	0.0M	0.0M	0.0M	0.6%	0.1%	3.2bp	0.1%	1.6bp
2CLCmesc_2_RR_S10	8.3M	4.8M	57.2%	4.1M	49.0%	0.7M	200.0bp	181.5bp	0.2M	0.2M	0.2M	0.0M	0.0M	0.0M	1.2%	0.2%	5.1bp	0.4%	3.5bp
2CLCmesc_2_S2	11.3M	1.8M	16.1%	1.5M	13.6%	0.3M	200.0bp	180.7bp	0.5M	0.5M	0.5M	0.0M	0.0M	0.0M	0.7%	0.1%	3.4bp	0.0%	1.5bp
2CLCmesc_3_RR_S11	13.2M	9.1M	68.6%	7.9M	59.5%	1.2M	200.0bp	184.7bp	1.1M	1.0M	1.0M	0.0M	0.0M	0.1M	1.1%	0.1%	4.2bp	0.2%	2.9bp
2CLCmesc_3_S5	20.6M	12.5M	60.9%	10.9M	53.3%	1.6M	200.0bp	186.3bp	3.7M	3.5M	3.5M	0.0M	0.0M	0.1M	0.7%	0.1%	2.4bp	0.1%	1.5bp
2CLCmesc_4_RR_S12	12.0M	5.6M	46.5%	4.8M	40.1%	0.8M	200.0bp	185.2bp	1.0M	0.9M	1.0M	0.0M	0.0M	0.1M	1.0%	0.1%	3.8bp	0.2%	2.5bp
2CLCmesc_4_S6	15.9M	9.4M	59.1%	8.1M	51.0%	1.3M	200.0bp	185.1bp	2.7M	2.5M	2.5M	0.0M	0.0M	0.1M	0.8%	0.0%	2.3bp	0.1%	1.5bp
2CLCmesc_5_RR_S13	6.7M	4.9M	72.6%	4.2M	62.7%	0.7M	200.0bp	186.9bp	1.1M	1.1M	1.1M	0.0M	0.0M	0.0M	0.9%	0.1%	3.5bp	0.2%	2.6bp
2CLCmesc_5_S7	16.9M	12.9M	76.4%	11.2M	66.2%	1.7M	200.0bp	186.9bp	4.0M	3.8M	3.9M	0.0M	0.0M	0.1M	0.7%	0.0%	2.2bp	0.1%	1.5bp
2CLCmesc_6_RR_S14	5.4M	1.9M	34.9%	1.5M	28.5%	0.3M	200.0bp	183.9bp	0.4M	0.3M	0.3M	0.0M	0.0M	0.0M	1.1%	0.1%	3.2bp	0.1%	2.5bp
2CLCmesc_6_S8	9.3M	3.8M	40.8%	3.2M	34.1%	0.6M	200.0bp	185.3bp	1.0M	0.9M	0.9M	0.0M	0.0M	0.0M	0.9%	0.0%	2.1bp	0.1%	1.6bp
combined_003	33.8M	16.0M	47.5%	14.1M	41.7%	1.9M	200.0bp	183.5bp	3.0M	2.8M	2.8M	0.0M	0.0M	0.1M	0.7%	0.1%	3.4bp	0.1%	1.9bp
combined_004	29.0M	17.2M	59.3%	15.0M	51.7%	2.2M	200.0bp	185.8bp	3.1M	2.8M	2.9M	0.0M	0.0M	0.2M	0.7%	0.1%	3.3bp	0.1%	2.0bp
combined_1	17.3M	5.0M	28.7%	4.4M	25.2%	0.6M	200.0bp	182.9bp	0.9M	0.8M	0.8M	0.0M	0.0M	0.1M	0.9%	0.1%	4.1bp	0.2%	2.8bp
combined_2	19.6M	6.6M	33.6%	5.6M	28.6%	1.0M	200.0bp	181.3bp	0.7M	0.6M	0.7M	0.0M	0.0M	0.1M	1.1%	0.1%	4.8bp	0.3%	3.4bp
combined_3	33.8M	21.6M	63.9%	18.8M	55.7%	2.8M	200.0bp	185.6bp	4.8M	4.5M	4.6M	0.1M	0.0M	0.2M	0.8%	0.1%	3.2bp	0.1%	2.4bp
combined_4	27.9M	15.0M	53.7%	12.9M	46.3%	2.1M	200.0bp	185.1bp	3.7M	3.4M	3.5M	0.0M	0.0M	0.1M	0.8%	0.1%	2.9bp	0.1%	2.1bp
combined_5	23.6M	17.8M	75.3%	15.4M	65.2%	2.4M	200.0bp	186.9bp	5.2M	4.9M	5.0M	0.1M	0.0M	0.2M	0.8%	0.1%	2.6bp	0.1%	2.0bp
combined_6	14.7M	5.7M	38.6%	4.7M	32.1%	1.0M	200.0bp	184.8bp	1.3M	1.3M	1.3M	0.0M	0.0M	0.0M	0.9%	0.0%	2.5bp	0.1%	2.0bp

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: star_summary_table_table

Sort	Column	Description	ID	Scale
\|\|	Total reads	Number of input reads	`star-total_reads`	read_count
\|\|	Aligned	Mapped reads	`star-mapped`	read_count
\|\|	Aligned	% Mapped reads	`star-mapped_percent`
\|\|	Uniq aligned	Uniquely mapped reads	`star-uniquely_mapped`	read_count
\|\|	Uniq aligned	% Uniquely mapped reads	`star-uniquely_mapped_percent`
\|\|	Multimapped	Multiple mapped reads	`star-multimapped`	read_count
\|\|	Avg. read len	Average input read length	`star-avg_input_read_length`
\|\|	Avg. mapped len	Average mapped length	`star-avg_mapped_read_length`
\|\|	Splices	Number of splices: Total	`star-num_splices`	read_count
\|\|	Annotated splices	Number of splices: Annotated (sjdb)	`star-num_annotated_splices`	read_count
\|\|	GT/AG splices	Number of splices: GT/AG	`star-num_GTAG_splices`	read_count
\|\|	GC/AG splices	Number of splices: GC/AG	`star-num_GCAG_splices`	read_count
\|\|	AT/AC splices	Number of splices: AT/AC	`star-num_ATAC_splices`	read_count
\|\|	Non-canonical splices	Number of splices: Non-canonical	`star-num_noncanonical_splices`	read_count
\|\|	Mismatch rate	Mismatch rate per base	`star-mismatch_rate`
\|\|	Del rate	Deletion rate per base	`star-deletion_rate`
\|\|	Del len	Deletion average length	`star-deletion_length`
\|\|	Ins rate	Insertion rate per base	`star-insertion_rate`
\|\|	Ins len	Insertion average length	`star-insertion_length`

Alignment Scores

Created with MultiQC

FastQC

Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Created with MultiQC

Sequence Quality Histograms

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

Created with MultiQC

Per Sequence Quality Scores

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

Created with MultiQC

Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

Per Sequence GC Content

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Created with MultiQC

Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Created with MultiQC

Sequence Length Distribution

The distribution of fragment sizes (read lengths) found. See the FastQC help

Created with MultiQC

Sequence Duplication Levels

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Created with MultiQC

Overrepresented sequences by sample

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

Created with MultiQC

Top overrepresented sequences

Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

Showing ⁰/₂₀ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT	44	5940931	0.5637%
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA	38	2743254	0.2603%
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	35	1011753	0.0960%
AGGCCCGGTGGGGAAGGTGGCTCGGGGGGGGCGGCGCGTCTCAGGGCGCG	20	842347	0.0799%
GGAAGGCCCGGTGGGGAAGGTGGCTCGGGGGGGGCGGCGCGTCTCAGGGC	12	276892	0.0263%
GGTTACCCAAAAAATGAATTTAAGTTCAATTTTAAACTTGCTAAAAAAAC	12	138855	0.0132%
GGCGAGCTCAGGGAGGACAGAAACCTCCCGTGGAGCAGAAGGGCAAAAGC	12	377885	0.0359%
GGTGGCGCACGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGACAGGAGGA	10	124094	0.0118%
GGTGAACTATGCCTGGGCAGGGCGAAGCCAGAGGAAACTCTGGTGGAGGT	10	226452	0.0215%
GCGAGCTCAGGGAGGACAGAAACCTCCCGTGGAGCAGAAGGGCAAAAGCT	10	229390	0.0218%
AGGTGGCTCGGGGGGGGCGGCGCGTCTCAGGGCGCGCCGAACCACCTCAC	9	130412	0.0124%
CCTCACCCGGCCCGGACACGGACAGGATTGACAGATTGATAGCTCTTTCT	8	384780	0.0365%
AGAAAAGTTACCACAGGGATAACTGGCTTGTGGCGGCCAAGCGTTCATAG	8	312887	0.0297%
TGGTGAACTATGCCTGGGCAGGGCGAAGCCAGAGGAAACTCTGGTGGAGG	8	98240	0.0093%
GGGCGAAGCCAGAGGAAACTCTGGTGGAGGTCCGTAGCGGTCCTGACGTG	8	113197	0.0107%
CCGGGCGTCGTCGCGCCGTCGGGTCCCGGGGGGACCGTCGGTCACGCGTC	8	161159	0.0153%
CGGTGGCGCACGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGACAGGAGG	8	147475	0.0140%
AGTGACGAAAAATAACAATACAGGACTCTTTCGAGGCCCTGTAATTGGAA	8	148611	0.0141%
GCAGAATTCACCAAGCGTTGGATTGTTCACCCACTAATAGGGAACGTGAG	6	170828	0.0162%
GCTCTTCCTATCATTGTGAAGCAGAATTCACCAAGCGTTGGATTGTTCAC	6	118576	0.0113%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fastqc_top_overrepresented_sequences_table_table

Sort	Column	Description	ID
\|\|	Reports	Number of FastQC reports where this sequence is founds as overrepresented	`fastqc-samples`
\|\|	Occurrences	Total number of occurrences of the sequence (among the samples where the sequence is overrepresented)	`fastqc-total_count`
\|\|	% of all reads	Total number of occurrences as the percentage of all reads (among samples where the sequence is overrepresented)	`fastqc-total_percent`

Adapter Content

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

Min:

Max:

Created with MultiQC

Software Versions

Software Versions lists versions of software tools extracted from file contents.

Group	Software	Version
FastQC	fastqc	`0.12.1`
GUNZIP_FASTA	gunzip	`1.1`
MTX_TO_H5AD	anndata	`0.10.8`
	pandas	`2.2.2`
	python	`3.12.5`
	scanpy	`1.10.2`
STAR_ALIGN	star	`2.7.10b`
Workflow	Nextflow	`24.10.0`
	nf-core/scrnaseq	`v3.1.0dev`

nf-core/scrnaseq Methods Description

Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/nf-core/scrnaseq

Methods

Data was processed using nf-core/scrnaseq v3.1.0dev (doi: 10.5281/zenodo.3568187) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

The pipeline was executed with Nextflow v24.10.0 (Di Tommaso et al., 2017) with the following command:

nextflow run ../pipeline -profile daisybio,keep_work,apptainer,mouse,star_plate -resume

References

Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192

Notes:

The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

nf-core/scrnaseq Workflow Summary

- this information is collected when the pipeline is started.URL: https://github.com/nf-core/scrnaseq

Input/output options

input: samplesheet.csv
outdir: star_plate

Mandatory arguments

aligner: star
barcode_whitelist: ../../data/reference/plate.txt
protocol: CB_UMI_Simple

Skip Tools

skip_cellbender: true

Reference genome options

fasta: ../../data/reference/mouse/GRCm39.genome.fa.gz
gtf: ../../data/reference/mouse/gencode.vM36.annotation.gtf
igenomes_base: /nfs/data/references/igenomes
save_align_intermeds: true
save_reference: true

STARSolo Options

star_feature: Gene Velocyto
star_index: ../../data/reference/mouse/star

Institutional config options

config_profile_contact: Johannes Kersting (@JohannesKersting)
config_profile_description: DaiSyBio cluster profile provided by nf-core/configs.
config_profile_url: https://www.mls.ls.tum.de/daisybio/startseite/

Generic options

trace_report_suffix: 2025-01-21_17-29-03

Core Nextflow options

configFiles: N/A
containerEngine: apptainer
launchDir: /nfs/data/COST_IBD/VASA-Seq/scrnaseq/run
profile: daisybio,keep_work,apptainer,mouse,star_plate
projectDir: /nfs/data/COST_IBD/VASA-Seq/scrnaseq/pipeline
runName: backstabbing_mercator
userName: n.trummer
workDir: /nfs/scratch/nf-core_work/nfs.data.COST_IBD.VASA-Seq.scrnaseq.run

v1.25.1

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About MultiQC

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FastQC

Sequence Counts

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Per Sequence Quality Scores

Per Base Sequence Content

Rollover for sample name

Per Sequence GC Content

Per Base N Content

Sequence Length Distribution

Sequence Duplication Levels

Overrepresented sequences by sample

Top overrepresented sequences

Adapter Content

Status Checks

Software Versions

nf-core/scrnaseq Methods Description

Methods

References

Notes:

nf-core/scrnaseq Workflow Summary

Toggle navigation v1.25.1

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General Statistics: Columns

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STAR: Summary Statistics: Columns

Alignment Scores

FastQC

Sequence Counts Help

Sequence Quality Histograms Help

Per Sequence Quality Scores Help

Per Base Sequence Content Help

Rollover for sample name

Per Sequence GC Content Help

Per Base N Content Help

Sequence Length Distribution

Sequence Duplication Levels Help

Overrepresented sequences by sample Help

Top overrepresented sequences

FastQC: Top overrepresented sequences: Columns

Adapter Content Help

Status Checks Help

Software Versions

nf-core/scrnaseq Methods Description

Methods

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Notes:

nf-core/scrnaseq Workflow Summary

v1.25.1

Highlight Samples

Rename Samples

Show / Hide Samples

Sequence Counts

Sequence Quality Histograms

Per Sequence Quality Scores

Per Base Sequence Content

Per Sequence GC Content

Per Base N Content

Sequence Duplication Levels

Overrepresented sequences by sample

Adapter Content

Status Checks